# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import glob
import logging
import os
import time
from argparse import Namespace

import numpy as np
import torch
from torch.utils.data import DataLoader, TensorDataset

from lightning_base import BaseTransformer, add_generic_args, generic_train
from transformers import glue_compute_metrics as compute_metrics
from transformers import glue_convert_examples_to_features as convert_examples_to_features
from transformers import glue_output_modes
from transformers import glue_processors as processors
from transformers import glue_tasks_num_labels


logger = logging.getLogger(__name__)


class GLUETransformer(BaseTransformer):

    mode = "sequence-classification"

    def __init__(self, hparams):
        if type(hparams) == dict:
            hparams = Namespace(**hparams)
        hparams.glue_output_mode = glue_output_modes[hparams.task]
        num_labels = glue_tasks_num_labels[hparams.task]

        super().__init__(hparams, num_labels, self.mode)

    def forward(self, **inputs):
        return self.model(**inputs)

    def training_step(self, batch, batch_idx):
        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
            inputs["token_type_ids"] = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

        outputs = self(**inputs)
        loss = outputs[0]

        lr_scheduler = self.trainer.lr_schedulers[0]["scheduler"]
        tensorboard_logs = {"loss": loss, "rate": lr_scheduler.get_last_lr()[-1]}
        return {"loss": loss, "log": tensorboard_logs}

    def prepare_data(self):
        "Called to initialize data. Use the call to construct features"
        args = self.hparams
        processor = processors[args.task]()
        self.labels = processor.get_labels()

        for mode in ["train", "dev"]:
            cached_features_file = self._feature_file(mode)
            if os.path.exists(cached_features_file) and not args.overwrite_cache:
                logger.info("Loading features from cached file %s", cached_features_file)
            else:
                logger.info("Creating features from dataset file at %s", args.data_dir)
                examples = (
                    processor.get_dev_examples(args.data_dir)
                    if mode == "dev"
                    else processor.get_train_examples(args.data_dir)
                )
                features = convert_examples_to_features(
                    examples,
                    self.tokenizer,
                    max_length=args.max_seq_length,
                    label_list=self.labels,
                    output_mode=args.glue_output_mode,
                )
                logger.info("Saving features into cached file %s", cached_features_file)
                torch.save(features, cached_features_file)

    def get_dataloader(self, mode: str, batch_size: int, shuffle: bool = False) -> DataLoader:
        "Load datasets. Called after prepare data."

        # We test on dev set to compare to benchmarks without having to submit to GLUE server
        mode = "dev" if mode == "test" else mode

        cached_features_file = self._feature_file(mode)
        logger.info("Loading features from cached file %s", cached_features_file)
        features = torch.load(cached_features_file)
        all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
        all_attention_mask = torch.tensor([f.attention_mask for f in features], dtype=torch.long)
        all_token_type_ids = torch.tensor([f.token_type_ids for f in features], dtype=torch.long)
        if self.hparams.glue_output_mode == "classification":
            all_labels = torch.tensor([f.label for f in features], dtype=torch.long)
        elif self.hparams.glue_output_mode == "regression":
            all_labels = torch.tensor([f.label for f in features], dtype=torch.float)

        return DataLoader(
            TensorDataset(all_input_ids, all_attention_mask, all_token_type_ids, all_labels),
            batch_size=batch_size,
            shuffle=shuffle,
        )

    def validation_step(self, batch, batch_idx):
        inputs = {"input_ids": batch[0], "attention_mask": batch[1], "labels": batch[3]}

        if self.config.model_type not in ["distilbert", "bart"]:
            inputs["token_type_ids"] = batch[2] if self.config.model_type in ["bert", "xlnet", "albert"] else None

        outputs = self(**inputs)
        tmp_eval_loss, logits = outputs[:2]
        preds = logits.detach().cpu().numpy()
        out_label_ids = inputs["labels"].detach().cpu().numpy()

        return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}

    def _eval_end(self, outputs) -> tuple:
        val_loss_mean = torch.stack([x["val_loss"] for x in outputs]).mean().detach().cpu().item()
        preds = np.concatenate([x["pred"] for x in outputs], axis=0)

        if self.hparams.glue_output_mode == "classification":
            preds = np.argmax(preds, axis=1)
        elif self.hparams.glue_output_mode == "regression":
            preds = np.squeeze(preds)

        out_label_ids = np.concatenate([x["target"] for x in outputs], axis=0)
        out_label_list = [[] for _ in range(out_label_ids.shape[0])]
        preds_list = [[] for _ in range(out_label_ids.shape[0])]

        results = {**{"val_loss": val_loss_mean}, **compute_metrics(self.hparams.task, preds, out_label_ids)}

        ret = {k: v for k, v in results.items()}
        ret["log"] = results
        return ret, preds_list, out_label_list

    def validation_epoch_end(self, outputs: list) -> dict:
        ret, preds, targets = self._eval_end(outputs)
        logs = ret["log"]
        return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

    def test_epoch_end(self, outputs) -> dict:
        ret, predictions, targets = self._eval_end(outputs)
        logs = ret["log"]
        # `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
        return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}

    @staticmethod
    def add_model_specific_args(parser, root_dir):
        BaseTransformer.add_model_specific_args(parser, root_dir)
        parser.add_argument(
            "--max_seq_length",
            default=128,
            type=int,
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )

        parser.add_argument(
            "--task",
            default="",
            type=str,
            required=True,
            help="The GLUE task to run",
        )
        parser.add_argument(
            "--gpus",
            default=0,
            type=int,
            help="The number of GPUs allocated for this, it is by default 0 meaning none",
        )

        parser.add_argument(
            "--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
        )

        return parser


def main():
    parser = argparse.ArgumentParser()
    add_generic_args(parser, os.getcwd())
    parser = GLUETransformer.add_model_specific_args(parser, os.getcwd())
    args = parser.parse_args()

    # If output_dir not provided, a folder will be generated in pwd
    if args.output_dir is None:
        args.output_dir = os.path.join(
            "./results",
            f"{args.task}_{time.strftime('%Y%m%d_%H%M%S')}",
        )
        os.makedirs(args.output_dir)

    model = GLUETransformer(args)
    trainer = generic_train(model, args)

    # Optionally, predict on dev set and write to output_dir
    if args.do_predict:
        checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "checkpoint-epoch=*.ckpt"), recursive=True)))
        model = model.load_from_checkpoint(checkpoints[-1])
        return trainer.test(model)


if __name__ == "__main__":
    main()
